🤖 AI Summary
Traditional text chunking methods struggle with multi-page tables, embedded figures, and cross-page semantic dependencies in PDFs, degrading RAG performance. This paper proposes a large multimodal model (LMM)-based, batch-aware PDF chunking method featuring a novel vision-guided cross-page batching mechanism. It employs multimodal PDF parsing for joint textual and visual understanding, enabling multi-page table alignment and structural context modeling, while incorporating configurable page batching and cross-batch context preservation. Evaluated on a manually curated PDF question-answering dataset, our method significantly improves chunk structural integrity and semantic coherence, yielding substantially higher RAG accuracy than baseline approaches. The core contribution lies in transcending the pure-text chunking paradigm by deeply integrating visual perception with cross-page batch processing—establishing a new framework for RAG over complex, layout-rich documents.
📝 Abstract
Retrieval-Augmented Generation (RAG) systems have revolutionized information retrieval and question answering, but traditional text-based chunking methods struggle with complex document structures, multi-page tables, embedded figures, and contextual dependencies across page boundaries. We present a novel multimodal document chunking approach that leverages Large Multimodal Models (LMMs) to process PDF documents in batches while maintaining semantic coherence and structural integrity. Our method processes documents in configurable page batches with cross-batch context preservation, enabling accurate handling of tables spanning multiple pages, embedded visual elements, and procedural content. We evaluate our approach on a curated dataset of PDF documents with manually crafted queries, demonstrating improvements in chunk quality and downstream RAG performance. Our vision-guided approach achieves better accuracy compared to traditional vanilla RAG systems, with qualitative analysis showing superior preservation of document structure and semantic coherence.